Autoencoder-Based on Anomaly Detection with Intrusion Scoring for Smart Factory Environments

Bae, Gimin; Jang, Sunggyun; Kim, Minseop; Joe, Inwhee

doi:10.1007/978-981-13-5907-1_44

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…In [8], G. Bae et al proposed a DL anomaly detection model using AutoEncoder (AE) having three encoder and decoder layers. The experiment was conducted on KDD Cup '99 dataset and the Korea steel company's real dataset, they achieved the accuracy ranging 84% to 100% on KDD Cup '99 and 95% on the real dataset.…”

Section: Related Workmentioning

confidence: 99%

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Sharma¹,

Sharma²,

Lal³

et al. 2023

Computers and Electrical Engineering

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Section: Related Workmentioning

confidence: 99%

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Sharma¹,

Sharma²,

Lal³

et al. 2023

Computers and Electrical Engineering

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“…SNN-IDS is having higher accuracy than FNN IDS but fails in handling adversarial attacks with below 50% detection value and concludes to be not much suitable for real-life applications [55]. Autoencoder (AE) deep learning-based anomaly detection algorithm for the smart factory environment is shown in [56]. The test has been conducted with three scenarios for real-time data from Korea steel companies for the experiment.…”

Section: B Deep Learning-based Solutionsmentioning

confidence: 99%

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

et al. 2021

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Industrial Internet of Things (IIoTs) are the extensions of the Internet of Things (IoTs) and have paved the way towards the industry revolution 4.0. IIoT accelerates the industry automation of internal and external working process including transport, manufacturing, and marketing units with a number of connected devices. Being the extension of IoT, it inherits the insecurities of the technology; however, this sensor-configured infrastructure of IIoT needs some extra effort to customize the existing security solutions. In spite of the reconstructions of security models, the scope of improved developments is open to detect unknown attacks. The present study helps the researchers to understand the cause for intrusion by classifying and comparing various attacks of each IIoT layers. The main focus of this survey is to analyze various security issues faced by IIoT and provide a comparative analysis on the available solutions to enhance the industrial IoTs' protection systems. This study also notifies some open research problems for academia, technologists, and researchers to flourish the IIoT domain and its security aspects.

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“…The authors of [22] introduce a new method for intrusion detection that relies on an incremental clustering algorithm and adopts the DBSCAN algorithm. The authors of [23] propose a new algorithm for attack detection based on an autoencoder. In [24], the authors present a new algorithm to prevent users from phishing.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection in Critical Infrastructures: A Literature Review

et al. 2021

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Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.

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Autoencoder-Based on Anomaly Detection with Intrusion Scoring for Smart Factory Environments

Cited by 9 publications

References 9 publications

Anomaly based network intrusion detection for IoT attacks using deep learning technique

Anomaly based network intrusion detection for IoT attacks using deep learning technique

A Taxonomy of Security Issues in Industrial Internet-of-Things: Scoping Review for Existing Solutions, Future Implications, and Research Challenges

Intrusion Detection in Critical Infrastructures: A Literature Review

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